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Year
2025
Tech Stack
R, RStudio, tidyverse, lubridate, R Markdown
Description
A county-level analysis examining how demographic characteristics, mobility
behaviour, education levels, and healthcare capacity contributed to disparities
in COVID-19 case rates across the United States.
Why this project matters
This analysis aims to understand why certain communities were disproportionately affected during the pandemic. It emphasises the role of data in informing public health decisions and identifying structural inequalities.
Key Features
Technical Highlights
Why this project matters
This analysis aims to understand why certain communities were disproportionately affected during the pandemic. It emphasises the role of data in informing public health decisions and identifying structural inequalities.
Key Features
- 🛒 Analysis of mobility patterns using Google Mobility data
- 👴 Assessment of age structure and demographic risk factors
- 🎓 Evaluation of education-level correlations with case rates
- 🏥 Examination of healthcare capacity
Technical Highlights
- 📊 County-level statistical analysis performed in R
- 📈 Interpretable visualisations for regional comparison
- 🧾 Reproducible reporting using R Markdown
- 🔗 Integration of multiple public datasets
My Role
- 📊 Performed exploratory and statistical analysis
- 🔗 Integrated public health and mobility datasets
- 🧠 Interpreted results for policy insights
- 📄 Authored reproducible reports